Authors:
Myriam Bounhas
1
and
Bilel Elayeb
2
Affiliations:
1
Emirates College of Technology, Abu Dhabi, United Arab Emirates, LARODEC Research Laboratory, ISG of Tunis, Tunis University and Tunisia
;
2
Emirates College of Technology, Abu Dhabi, United Arab Emirates, RIADI Research Laboratory, ENSI, Manouba University and Tunisia
Keyword(s):
Information Retrieval, Analogical Proportions, Similarity, Agreement, Disagreement, Analogical Relevance.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Hybrid Intelligent Systems
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Natural Language Processing
;
Pattern Recognition
;
Soft Computing
;
Symbolic Systems
Abstract:
This paper describes a new matching model based on analogical proportions useful for domain-specific Information Retrieval (IR). We first formalize the relationship between documents terms and query terms through analogical proportions and we propose a new analogical inference to evaluate document relevance for a given query. Then we define the analogical relevance of a document in the collection by aggregating two scores: the Agreement, measured by the number of common terms, and the Disagreement, measured by the number of different terms. The disagreement degree is useful to filter documents out from the response (retrieved documents), while the agreement score is convenient for document relevance confirmation. Experiments carried out on three IR Glasgow test collections highlight the effectiveness of the model if compared to the known efficient Okapi IR model.